General Instructions:

a.Insert your answer after each question.

b.Attach a screenshot of the result generated by excel to support your interpretation.

I.Use the dataset Real estate.csv, click the link https://www.kaggle.com/quantbruce/real-estate-price-prediction to retrieve the file. Answer the following questions:

1.Do transaction date,house age, distance to the nearest MRT station,number of convenience stores, latitude, and longitude explain the changes in the house's price per unit area? Justify your answer by interpreting the result.

2.Which among the predictor variables would stimulate an increase in the house's price per unit area? Explain your answer.

3.a. What price of the house per unit area would one expect if it has the following

values :

transaction date = 2013

house age = 43.25

distance to the nearest MRT station = 712.14

number of convenience stores = 5

latitude = 24.71435

longitude = 122.8441

b. Write the equation of the regression model.

II.Use the dataset Amazon.csv, click the link https://www.kaggle.com/salmanfaroz/amazon-stock-price-1997-to-2020 to retrieve the file. Read the description and context found in the link to understand the dataset.

4.Generate the best demand forecasting model. This valuable insight can help stock exchange traders to yield significant profit for the investors.

5.make a forecasting graph.

6.Use the model to forecast the closing trade value for the next 2-3 weeks.